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Special Issue:
农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
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| Early-season crop type mapping using 30-m reference time series |
| HAO Peng-yu1, 2, TANG Hua-jun1, CHEN Zhong-xin1, MENG Qing-yan3, KANG Yu-peng3 |
1 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Shenzhen Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, P.R.China
3 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, P.R.China |
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Abstract Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction, but the lack of ground-surveyed training samples is the main challenge for crop type identification. Although reference time series based method (RBM) has been proposed to identify crop types without the use of ground-surveyed training samples, the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution. As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series, we improved the RBM by generating reference normalized difference vegetation index (NDVI)/enhanced vegetation index (EVI) time series at 30-m resolution (30-m RBM) using both Landsat and Sentinel-2 data, then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season. As a test case, we tried to use the 30-m RBM to identify major crop types in Hengshui, China at early season of 2018, the results showed that when the time series of the entire growing season were used for classification, overall classification accuracies of the 30-m RBM were higher than 95%, which were similar to the accuracies acquired using the ground-surveyed training samples. In addition, cotton, spring maize and summer maize distribution could be accurately generated 8, 6 and 8 weeks before their harvest using the 30-m RBM; but winter wheat can only be accurately identified around the harvest time phase. Finally, NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases. Comparing with the previous RBM, advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop classification; while, samples collected from multiple years should be further used so that the reference time series could contain more crop growth conditions.
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Received: 15 April 2019
Accepted:
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| Fund: The study was supported by the China National Key S&T Project of High Resolution Earth Observation System (30-Y20A07-9003-17/18) and the National Natural Science Foundation of China (41801359). |
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Corresponding Authors:
Correspondence TANG Hua-jun, E-mail: tanghuajun@caas.cn
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| About author:
HAO Peng-yu, Mobile: +86-13718668296, E-mail: haopy8296@163.com; |
Cite this article:
HAO Peng-yu, TANG Hua-jun, CHEN Zhong-xin, MENG Qing-yan, KANG Yu-peng.
2020.
Early-season crop type mapping using 30-m reference time series. Journal of Integrative Agriculture, 19(7): 1897-1911.
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Bruzzone L, Roli F, Serpico S B. 1995. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection. IEEE Transactions on Geoscience and Remote Sensing, 33, 1318–1321.
Cai Y P, Guan K Y, Peng J, Wang S W, Seifert C, Wardlow B, Li Z. 2018. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment, 210, 35–47.
Chang P C, Lin C H, Chen M H. 2016. A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems. Algorithms, 9, 47.
Chockalingam J, Mondal S. 2017. Fractal-based pattern extraction from time-series NDVI data for feature identification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 5258–5264.
Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.
Dong J, Xiao X, Menarguez M A, Zhang G, Qin Y, Thau D, Biradar C, Moore B III. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142–154.
ESA (European Space Agency). 2016. Sentinel-2 for agriculture. [2019-03-15]. http://www.esa-sen2agri.org/
Forkuor G, Conrad C, Thiel M, Ullmann T, Zoungrana E. 2014. Integration of optical and synthetic aperture radar imagery for improving crop mapping in northwestern Benin, West Africa. Remote Sensing, 6, 6472–6499.
Google. 2015. Google earth engine. [2019-03-15]. https://developers.google.com/earth-engine/
Hao P, Chen Z, Tang H, Li D, Li H. 2019. New workflow of plastic-mulched farmland mapping using multi-temporal Sentinel-2 data. Remote Sensing, 11, 1353.
Hao P, Löw F, Biradar C. 2018a. Annual cropland mapping using reference landsat time series - a case study in Central Asia. Remote Sensing, 10, 2057.
Hao P, Tang H, Chen Z, Liu Z. 2018b. Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data. PeerJ, 6, 5431.
Hao P, Wang L, Niu Z, Aablikim A, Huang N, Xu S, Chen F. 2014. The potential of time series merged from landsat-5 TM and HJ-1 CCD for crop classification: A case study for Bole and Manas counties in Xinjiang, China. Remote Sensing, 6, 7610–7631.
Hao P, Wang L, Zhan Y, Niu Z. 2016a. Using moderate-resolution temporal NDVI profiles for high-resolution crop mapping in years of absent ground reference data: A case study of Bole and Manas counties in Xinjiang, China. Isprs International Journal of Geo-Information, 5, 67.
Hao P, Wang L, Zhan Y, Wang C, Niu Z, Wu M. 2016b. Crop classification using crop knowledge of the previous year: Case study in Southwest Kansas, USA. European Journal of Remote Sensing, 49, 1061–1077.
Hao P, Wu M, Niu Z, Wang L, Zhan Y. 2018c. Estimation of different data compositions for early-season crop type classification. PeerJ, 6, 4834.
Hao P Y, Zhan Y L, Wang L, Niu Z, Shakir M. 2015. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA. Remote Sensing, 7, 5347–5369.
Harald V D W, Freek V D M. 2016. Sentinel-2A MSI and landsat 8 OLI provide data continuity for geological remote sensing. Remote Sensing, 8, 883.
Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.
Im J, Lu Z, Rhee J, Jensen J R. 2012a. Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes. Geocarto International, 27, 373–393.
Im J, Lu Z, Rhee J, Quackenbush L J. 2012b. Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sensing of Environment, 117, 102–113.
Knauer K, Gessner U, Fensholt R, Forkuor G, Kuenzer C. 2017. Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: The role of population growth and implications for the environment. Remote Sensing, 9, 132.
Korhonen L, Hadi, Packalen P, Rautiainen M. 2017. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment, 195, 259–274.
Lhermitte S, Verbesselt J, Verstraeten W W, Coppin P. 2011. A comparison of time series similarity measures for classification and change detection of ecosystem dynamics. Remote Sensing of Environment, 115, 3129–3152.
Li J, Roy D. 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9, 902.
Liu H, Guo H, Yang L, Wu L, Li F, Li S, Ni P, Liang X. 2015. Occurrence and formation of high fluoride groundwater in the Hengshui area of the North China Plain. Environmental Earth Sciences, 74, 2329–2340.
Löw F, Biradar C, Dubovyk O, Fliemann E, Akramkhanov A, Vallejo A N, Waldner F. 2018. Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series. Giscience & Remote Sensing, 55, 539–567.
Luo C, Liu H, Fu Q, Guan H, Ye Q, Zhang X, Kong F. 2020. Mapping the fallowed area of paddy fields on Sanjiang Plain of Northeast China to assist water security assessments. Journal of Integrative Agriculture, 19, 1885–1896.
Mondal S, Jeganathan C. 2018. Mountain agriculture extraction from time-series MODIS NDVI using dynamic time warping technique. International Journal of Remote Sensing, 39, 3679–3704.
NASA (National Aeronautics and Space Administration). 2017. Harmonized Landsat-8 and Sentinel-2. [2018-11-12]. https://hls.gsfc.nasa.gov/
Potgieter A B, Apan A, Hammer G, Dunn P. 2010. Early-season crop area estimates for winter crops in NE Australia using MODIS satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 380–387.
Rouse J W, Haas R H, Schell J A, Deering D W, Harlan J C. 1974. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation. NASA/GSFC, USA. pp. 1–137.
Schmidt T, Schuster C, Kleinschmit B, Foerster M. 2014. Evaluating an intra-annual time series for grassland classification - how many acquisitions and what seasonal origin are optimal? IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3428–3439.
Skakun S, Franch B, Vermote E, Roger J C, Becker-Reshef I, Justice C, Kussul N. 2017. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment, 195, 244–258.
Sothe C, de Almeida C M, Liesenberg V, Schimalski M B. 2017. Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in southern Brazil. Remote Sensing, 9, 838.
Torres-Sanchez J, Pena J M, de Castro A I, Lopez-Granados F. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113.
Vaudour E, Noirot-Cosson P E, Membrive O. 2015. Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images. International Journal of Applied Earth Observation and Geoinformation, 42, 128–141.
Wang S, Azzari G, Lobell D B. 2019. Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment, 222, 303–317.
Wardlow B D, Egbert S L. 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sensing of Environment, 112, 1096–1116.
Wardlow B D, Egbert S L. 2010. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas. International Journal of Remote Sensing, 31, 805–830.
Wardlow B D, Egbert S L, Kastens J H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108, 290–310.
De Wit A J W, Clevers J G P W. 2004. Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 25, 4091–4112.
Xie H, Cheng L, Lv T. 2017. Factors influencing farmer willingness to fallow winter wheat and ecological compensation standards in a groundwater funnel area in Hengshui, Hebei Province, China. Sustainability, 9, 1–18.
Xiong J, Thenkabail P, Tilton J, Gumma M, Teluguntla P, Oliphant A, Congalton R, Yadav K, Gorelick N. 2017. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine. Remote Sensing, 9, 1065.
Yu L, Wang J, Clinton N, Xin Q, Zhong L, Chen Y, Gong P. 2013. FROM-GC: 30-m global cropland extent derived through multisource data integration. International Journal of Digital Earth, 6, 521–533.
Zhong L, Hu L, Zhou H. 2019. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221, 430–443.
Zhong L H, Gong P, Biging G S. 2012. Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California’s Central Valley. Photogrammetric Engineering and Remote Sensing, 78, 799–813.
Zhong L H, Gong P, Biging G S. 2014. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment, 140, 1–13.
Zhong Y, Zhang L. 2012. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 50, 894–909.
Zhou F Q, Zhang A N, Townley-Smith L. 2013. A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level. ISPRS Journal of Photogrammetry and Remote Sensing, 84, 114–129.
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